# Partially tree-based regression model function The `pltr.glm` function is designed to fit an hybrid glm model with an additive tree part on a glm scale. ```r pltr.glm(data, Y.name, X.names, G.names, family = "binomial", args.rpart = list(cp = 0, minbucket = 20, maxdepth = 10), epsi = 0.001, iterMax = 5, iterMin = 3, verbose = TRUE) ``` ## Arguments - `data`: a data frame containing the variables in the model - `Y.name`: the name of the dependent variable - `X.names`: the names of independent variables to consider in the linear part of the `glm` - `G.names`: the names of independent variables to consider in the tree part of the hybrid `glm`. - `family`: the `glm` family considered depending on the type of the dependent variable. - `args.rpart`: a list of options that control details of the rpart algorithm. `minbucket`: the minimum number of observations in any terminal node; `cp`: complexity parameter (Any split that does not decrease the overall lack of fit by a factor of cp is not attempted); `maxdepth`: the maximum depth of any node of the final tree, with the root node counted as depth 0. ... See `rpart.control` for further details - `epsi`: a treshold value to check the convergence of the algorithm - `iterMax`: the maximal number of iteration to consider - `iterMin`: the minimum number of iteration to consider - `verbose`: Logical; TRUE for printing progress during the computation (helpful for debugging) ## Details The `pltr.glm` function use an itterative procedure to fit the linear part of the `glm` and the tree part. The tree obtained at the convergence of the procedure is a maximal tree which overfits the data. It's then mandatory to prunned back this tree by using one of the proposed criteria (`BIC`, `AIC` and `CV`). ## Returns A list with four elements: - **fit**: the glm fitted on the confounding factors at the end of the iterative algorithm - **tree**: the maximal tree obtained at the end of the algorithm - **nber_iter**: the number of iterations used by the algorithm - **Timediff**: The execution time of the iterative procedure ## References Mbogning, C., Perdry, H., Toussile, W., Broet, P.: A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities. Journal of Clinical Bioinformatics 4:6, (2014) Terry M. Therneau, Elizabeth J. Atkinson (2013) An Introduction to Recursive Partitioning Using the `RPART` Routines. Mayo Foundation. Chen, J., Yu, K., Hsing, A., Therneau, T.M.: A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genetic Epidemiology 31, 238-251 (2007) ## Author(s) Cyprien Mbogning and Wilson Toussile ## Note The tree obtained at the end of these itterative procedure usually overfits the data. It's therefore mendatory to use either `best.tree.BIC.AIC` or `best.tree.CV` to prunne back the tree. ## See Also `rpart` ## Examples ```r data(burn) args.rpart <- list(minbucket = 10, maxdepth = 4, cp = 0, maxcompete = 0, maxsurrogate = 0) family <- "binomial" X.names = "Z2" Y.name = "D2" G.names = c('Z1','Z3','Z4','Z5','Z6','Z7','Z8','Z9','Z10','Z11') pltr.burn <- pltr.glm(burn, Y.name, X.names, G.names, args.rpart = args.rpart, family = family, iterMax = 4, iterMin = 3, verbose = FALSE) ## Not run: ## load the data set data(data_pltr) ## set the parameters args.rpart <- list(minbucket = 40, maxdepth = 10, cp = 0) family <- "binomial" Y.name <- "Y" X.names <- "G1" G.names <- paste("G", 2:15, sep="") ## build a maximal tree fit_pltr <- pltr.glm(data_pltr, Y.name, X.names, G.names, args.rpart = args.rpart, family = family,iterMax = 5, iterMin = 3) plot(fit_pltr$tree, main = 'MAXIMAL TREE') text(fit_pltr$tree, minlength = 0L, xpd = TRUE, cex = .6) ## End(Not run) ```

pltr.glm function